Plot time series for the same place

library(googlesheets)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

df <- gs_title("TB systematic review plan")

## Sheet successfully identified: "TB systematic review plan"

StudyArea <- df %>% gs_read(ws = 'Study area table')

## Accessing worksheet titled 'Study area table'.

## Parsed with column specification:
## cols(
##   StudyID = col_integer(),
##   SubID = col_integer(),
##   `Third-order Administrative division` = col_character(),
##   `Second-order administrative division` = col_character(),
##   `First-Order Administrative division` = col_character(),
##   Country = col_character(),
##   Latitude = col_character(),
##   Longitude = col_character(),
##   `Elevation (m)` = col_character()
## )

CaseCount <- df %>% gs_read(ws = 'Time series table')

## Accessing worksheet titled 'Time series table'.

## Parsed with column specification:
## cols(
##   StudyID = col_integer(),
##   SubID = col_integer(),
##   Date = col_character(),
##   CaseCount = col_number()
## )

CaseCount$Date <- as.Date(CaseCount$Date)

# combine study ID andn subID
CaseCount$ID <- paste(CaseCount$StudyID, CaseCount$SubID, sep = "_")

unique.place <- unique(StudyArea[,1:6])

Beijing

library(ggplot2)
# Beijing
cities <- CaseCount[CaseCount$ID %in% c("932_1","1514_1"),]
ggplot(cities, aes(x = Date, y = CaseCount, col = ID)) + geom_line() + theme_bw() + ggtitle("Beijing")

Hongkong

cities <- CaseCount[CaseCount$ID %in% c("932_2","1077_1"),]
ggplot(cities, aes(x = Date, y = CaseCount, col = ID)) + geom_line() + theme_bw() + ggtitle("Hong Kong")

Wuhan

cities <- CaseCount[CaseCount$ID %in% c("1431_1","1431_2","398_1"),]

# sum up smear+ and smear- cases in 1431
cities <- aggregate(CaseCount ~ StudyID + Date, cities, sum)
ggplot(cities, aes(x = Date, y = CaseCount, col = as.factor(StudyID))) + geom_line() + theme_bw() + ggtitle("Wuhan")

Jiangxi

cities <- CaseCount[CaseCount$ID %in% c("165_1","1419_1"),]
ggplot(cities, aes(x = Date, y = CaseCount, col = ID)) + geom_line() + theme_bw() + ggtitle("Jiangxi")

China

cities <- CaseCount[CaseCount$ID %in% paste(c(196, 291, 428, 498, 730, 936, 1329, 1451, 1475, 1503),"_1", sep = ""),]
China <- ggplot(cities, aes(x = Date, y = CaseCount, col = ID)) + geom_line() + theme_bw() + ggtitle("China")
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
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##     last_plot

## The following object is masked from 'package:stats':
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##     filter

## The following object is masked from 'package:graphics':
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##     layout

ggplotly(China)

Iran

cities <- CaseCount[CaseCount$ID %in% paste(c(36, 42, 523, 1080),"_1", sep = ""),]
ggplot(cities, aes(x = Date, y = CaseCount, col = ID)) + geom_line() + theme_bw() + ggtitle("Iran")

Mongolia

cities <- CaseCount[CaseCount$ID %in% c("243_2", "721_1"),]
cities.plot <- ggplot(cities, aes(x = Date, y = CaseCount, col = ID)) + geom_line() + theme_bw() + ggtitle("Mongolia")
ggplotly(cities.plot)

United States

cities <- CaseCount[CaseCount$ID %in% paste(c(441, 583, 1252),"_1", sep = ""),]
ggplot(cities, aes(x = Date, y = CaseCount, col = ID)) + geom_line() + theme_bw() + ggtitle("United States")